Comparative evaluation of
            <scp>AI</scp>
            ‐based intelligent
            <scp>GEP</scp>
            and
            <scp>ANFIS</scp>
            models in prediction of thermophysical properties of
            <scp>
              Fe
              <sub>3</sub>
              O
              <sub>4</sub>
            </scp>
            ‐coated
            <scp>MWCNT</scp>
            hybrid nanofluids for potential application in energy systems

Sharma, Prabhakar; Said, Zafar; Memon, Saim; Elavarasan, Rajvikram Madurai; Khalid, Mohammad; Nguyen, Xuan Phuong; Arıcı, Müslüm; Hoang, Anh Tuan; Nguyen, Lan Huong

doi:10.1002/er.8010

Cited by 44 publications

(9 citation statements)

References 77 publications

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“…Six quantitative metrics, including the Pearson coefficient of determination (R 2 ), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative square error (RRSE) 34 , and Nash–Sutcliffe efficiency coefficient (NSE) were used for performance analysis of the model in the testing dataset. It’s worth noting that the NSE has been used for the performance evaluation of ML models in different fields (e.g., hydrology, physics) 33 , 35 – 37 and has been confirmed as a more reliable efficiency index compared with R 2 33 . Therefore, we suggested it for evaluation of the results of this study.…”

Section: Methodsmentioning

confidence: 99%

Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model

Asadi

Tartibian

Moni

2023

Sci Rep

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One of the important concerns in the field of exercise immunology is determining the appropriate intensity and duration of exercise to prevent suppression of the immune system. Adopting a reliable approach to predict the number of white blood cells (WBCs) during exercise can help to identify the appropriate intensity and duration. Therefore, this study was designed to predict leukocyte levels during exercise with the application of a machine-learning model. We used a random forest (RF) model to predict the number of lymphocytes (LYMPH), neutrophils (NEU), monocytes (MON), eosinophils, basophils, and WBC. Intensity and duration of exercise, WBCs values before exercise training, body mass index (BMI), and maximal aerobic capacity (VO2 max) were used as inputs and WBCs values after exercise training were assessed as outputs of the RF model. In this study, the data was collected from 200 eligible people and K-fold cross-validation was used to train and test the model. Finally, model efficiency was assessed using standard statistics (root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative square error (RRSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency coefficient (NSE)). Our findings revealed that the RF model performed well for predicting the number of WBC with RMSE = 0.94, MAE = 0.76, RAE = 48.54, RRSE = 48.17, NSE = 0.76, and R2 = 0.77. Furthermore, the results showed that intensity and duration of exercise are more effective parameters than BMI and VO2 max to predict the number of LYMPH, NEU, MON, and WBC during exercise. Totally, this study developed a novel approach based on the RF model using the relevant and accessible variables to predict WBCs during exercise. The proposed method can be applied as a promising and cost-effective tool for determining the correct intensity and duration of exercise in healthy people according to the body’s immune system response.

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Section: Methodsmentioning

confidence: 99%

Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model

Asadi

Tartibian

Moni

2023

Sci Rep

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“…Machine learning can help address many of these issues [37] and this paper has developed a framework and approach using machine learning that will be able to detect several banking malware variants. Although other researchers [26][27][28] have done some experimental work on detecting malware, there is little to no research that aims to detect a range of malware variants by only training one dataset, i.e., one malware variant.…”

Section: Problem Statementmentioning

confidence: 99%

An Investigation to Detect Banking Malware Network Communication Traffic Using Machine Learning Techniques

Kazi

Woodhead

Gan

2022

JCP

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Banking malware are malicious programs that attempt to steal confidential information, such as banking authentication credentials, from users. Zeus is one of the most widespread banking malware variants ever discovered. Since the Zeus source code was leaked, many other variants of Zeus have emerged, and tools such as anti-malware programs exist that can detect Zeus; however, these have limitations. Anti-malware programs need to be regularly updated to recognise Zeus, and the signatures or patterns can only be made available when the malware has been seen. This limits the capability of these anti-malware products because they are unable to detect unseen malware variants, and furthermore, malicious users are developing malware that seeks to evade signature-based anti-malware programs. In this paper, a methodology is proposed for detecting Zeus malware network traffic flows by using machine learning (ML) binary classification algorithms. This research explores and compares several ML algorithms to determine the algorithm best suited for this problem and then uses these algorithms to conduct further experiments to determine the minimum number of features that could be used for detecting the Zeus malware. This research also explores the suitability of these features when used to detect both older and newer versions of Zeus as well as when used to detect additional variants of the Zeus malware. This will help researchers understand which network flow features could be used for detecting Zeus and whether these features will work across multiple versions and variants of the Zeus malware.

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“… [ 36 ] CuO-EGB Correlation, ANN Temp., VF, Size 0.9997 Viscosity measurements of volume concentrations of 1%–4% are conducted in the temperature range of 293 K–353 K [ 38 ] MWCNT-OB ANN, RSM Temp., VF, SR NA Four optimization case studies were conducted, focusing on objective functions derived from nanofluid (NF) applications in various industries [ 41 ] Al 2 O 3 , CuO, SiO 2 , TiO 2 PGDNN Density, Size, VF, Temp., μ base-fluid , μ simulated , 0.9961 PGDNN approach integrates data-driven models with physics-based theoretical models to leverage their complementary strengths and enhance the modeling of physical processes [ 47 ] Al 2 O 3 , MWCNT, GNP-WB MLPNN Temp., VF, Nanofluid 0.9998 For VFs of (0.1%, 0.25%, 0.5%, 0.75%, and 1%) and temperature range 30–80 °C [ 48 ] ZrSiO 4 ANFIS Temp., VF 1 C-clustering is used in obtaining membership function. [ 49 ] Fe 3 O 4 -coated MWCNT ANFIS, GEP Temp., VF 0.9702 Genetic algorithm was used and studied. a WB = water based, OB = oil based, VF = volume fraction, SR = shear rate, EGB = Ethylene glycol based, MWCNT = multi-walled carbon nanotubes.…”

Section: Introductionmentioning

confidence: 99%

Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning

AbuShanab

Al‐Ammari

Gowid

et al. 2023

Heliyon

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Comparative evaluation of AI ‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe ₃ O ₄ ‐coated MWCNT hybrid nanofluids for potential application in energy systems

Cited by 44 publications

References 77 publications

Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model

Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model

An Investigation to Detect Banking Malware Network Communication Traffic Using Machine Learning Techniques

Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning

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Comparative evaluation of AI ‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe 3 O 4 ‐coated MWCNT hybrid nanofluids for potential application in energy systems

Cited by 44 publications

References 77 publications

Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model

Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model

An Investigation to Detect Banking Malware Network Communication Traffic Using Machine Learning Techniques

Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning

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Product

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Comparative evaluation of AI ‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe ₃ O ₄ ‐coated MWCNT hybrid nanofluids for potential application in energy systems